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- W3110827398 abstract "Scholarly venue recommendation is an emerging field due to a rapid surge in the number of scholarly venues concomitant with exponential growth in interdisciplinary research and cross collaboration among researchers. Finding appropriate publication venues is confronted as one of the most challenging aspects in paper publication as a larger proportion of manuscripts face rejection due to a disjunction between the scope of the venue and the field of research pursued by the research article. We present CLAVER--an integrated framework of Convolutional Layer, bi-directional LSTM with an Attention mechanism-based scholarly VEnue Recommender system. The system is the first of its kind to integrate multiple deep learning-based concepts, that requires only the abstract and the title of a manuscript to identify academic venues. An extensive and exhaustive set of experiments conducted on the DBLP dataset certify that the postulated model CLAVER performs better than most of the modern techniques as entrenched by standard metrics such as stability, accuracy, MRR, average venue quality, [email protected], [email protected] and diversity." @default.
- W3110827398 created "2020-12-21" @default.
- W3110827398 creator A5004907604 @default.
- W3110827398 creator A5072519186 @default.
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- W3110827398 date "2021-06-01" @default.
- W3110827398 modified "2023-09-27" @default.
- W3110827398 title "CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation" @default.
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- W3110827398 doi "https://doi.org/10.1016/j.ins.2020.12.024" @default.
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